Machine Learning Detects Multiplicity of the First Stars in Stellar Archaeology Data

Tilman Hartwig, Miho N. Ishigaki, Chiaki Kobayashi, Nozomu Tominaga, Ken’ichi Nomoto

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In unveiling the nature of the first stars, the main astronomical clue is the elemental compositions of the second generation of stars, observed as extremely metal-poor (EMP) stars, in the Milky Way. However, no observational constraint was available on their multiplicity, which is crucial for understanding early phases of galaxy formation. We develop a new data-driven method to classify observed EMP stars into mono- or multi-enriched stars with support vector machines. We also use our own nucleosynthesis yields of core-collapse supernovae with mixing fallback that can explain many of the observed EMP stars. Our method predicts, for the first time, that 31.8% ± 2.3% of 462 analyzed EMP stars are classified as mono-enriched. This means that the majority of EMP stars are likely multi-enriched, suggesting that the first stars were born in small clusters. Lower-metallicity stars are more likely to be enriched by a single supernova, most of which have high carbon enhancement. We also find that Fe, Mg. Ca, and C are the most informative elements for this classification. In addition, oxygen is very informative despite its low observability. Our data-driven method sheds a new light on solving the mystery of the first stars from the complex data set of Galactic archeology surveys.
Original languageEnglish
Article number20
Pages (from-to)1-18
Number of pages18
JournalThe Astrophysical Journal
Issue number1
Publication statusPublished - 22 Mar 2023


  • 310
  • Galaxies and Cosmology


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